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Distributed Optimal Control of Large-Scale Wind Farm Clusters: Optimal Active and Reactive Power Control, and Fault Ride Through [Mīkstie vāki]

, (Professor, School of Electronics, Electrical Engineering, and Computer Science, Queens University Belfast, UK), (College of Electrical and Inform), (Professor, College of Electrical and Information Engineering, Hunan University, China), ,
  • Formāts: Paperback / softback, 446 pages, height x width: 229x152 mm, weight: 450 g
  • Sērija : Wind Energy Engineering
  • Izdošanas datums: 30-May-2025
  • Izdevniecība: Academic Press Inc
  • ISBN-10: 0443292345
  • ISBN-13: 9780443292347
  • Mīkstie vāki
  • Cena: 197,76 €
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  • Formāts: Paperback / softback, 446 pages, height x width: 229x152 mm, weight: 450 g
  • Sērija : Wind Energy Engineering
  • Izdošanas datums: 30-May-2025
  • Izdevniecība: Academic Press Inc
  • ISBN-10: 0443292345
  • ISBN-13: 9780443292347
Distributed Optimal Control of Large-Scale Wind Farm Clusters: Optimal Active and Reactive Power Control, and Fault Ride Through, a new volume in the Elsevier Wind Energy Engineering series, explores the latest advances in distributed optimal control of large-scale wind farm clusters, also describing distributed optimal control techniques for high voltage ride through (HVRT). Both mathematical formulations and algorithm details are provided, along with MATLAB codes to replicate and implement distributed optimal control schemes. This is a valuable resource for anyone interested in the operation, control, and integration of wind power plants, wind farms, and electricity grids, both at research and operational levels.

Researchers, faculty, scientists, engineers, R&D, and other industry professionals, as well as graduate and postgraduate students studying and working in wind energy will find this comprehensive resource a valuable addition to their work.
Section I - Introduction
1. Introduction to Large-Scale Wind Power Integration

Section II - Optimal Active Power Control of Large-Scale Wind Farm Clusters
2. Bi-Level Decentralized Active Power Control for Large-Scale Wind Farm
Clusters
3. Optimal Active Power Control Based on MPC for DFIG-based Wind Farm
Equipped with Distributed Energy Storage Systems
4. Hierarchical Active Power Control of DFIG-based Wind Farm with Distributed
Energy Storage Systems based on Alternating Direction Method of Multipliers
(ADMM)
5 Hierarchical Optimal Control for Synthetic Inertial Response of Wind Farm
Based on Alternating Direction Method of Multipliers (ADMM)

Section III - Optimal Active and Reactive Power Control of Large-Scale Wind
Farm Clusters
6. Bi-Level Decentralized Active and Reactive Power Control for Large-Scale
Wind Farm Cluster
7. Two-Tier Combined Active and Reactive Power Control for VSCHVDC Connected
Large-Scale Wind Farm Cluster based on Alternating Direction Method of
Multipliers (ADMM)
8. Distributed Optimal Active and Reactive Power Control for Wind Farms Based
on ADMM
9. ADMM-based Distributed Active and Reactive Power Control for Regional AC
Grids with Wind Farms

Section IV - Optimal Voltage Control of Large-Scale Wind Farm Clusters
10. Distributed Voltage Control based on ADMM for Large Scale Wind Farm
Cluster connected to VSC HVDC
11. Distributed Optimal Voltage Control for VSC-HVDC Connected Large-Scale
Wind Farm Cluster Based on Analytical Target Cascading Method
12. Adaptive Droop-based Hierarchical Optimal Voltage Control Scheme for
VSC-HVDC Connected Offshore Wind Farm
13. Distributed Optimal Voltage Control Strategy for AC Grid with DC
Connection and Offshore Wind Farms Based on Alternating Direction Method of
Multipliers (ADMM)

Section V - Fault Ride Through of Wind Farm Clusters
14. Coordinated Droop Control and Adaptive Model Predictive Control for
Enhancing HVRT and Post-Event Recovery of Large-Scale Wind Farms
15. Hierarchical Event-Triggered MPC-Based Coordinated Control for HVRT and
Voltage Restoration of Large-Scale Wind Farms
16. Coordinated Voltage Support Control for Enhancing LVRT Capability of
Large-Scale Wind Farms
Qiuwei Wu received the PhD degree in Electrical Engineering from Nanyang Technological University, Singapore, in 2009. He is a professor with the School of Electronics, Electrical Engineering, and Computer Science (EEECS), Queens University Belfast, the UK. His research interests are distributed optimal operation and control of low carbon power and energy systems, including distributed optimal control of wind power, optimal operation of active distribution networks, and optimal operation and planning of integrated energy systems.

Sheng Huang received the M.S. and Ph.D. degrees in electrical engineering from the College of Electrical and Information Engineering, Hunan University, Changsha, China, in 2012 and 2016, respectively. Since 2023, he has been a Professor with College of Electrical and Information Engineering, Hunan University. His research interests include renewable energy generation, modeling and integration study of wind power, and permanent magnet synchronous motor systems.

Juan Wei is currently a Postdoc with the College of Electrical and Information Engineering, Hunan University, China. She received her B.S. and M.S. degrees in electrical engineering from the North China Electric Power University, Beijing, China, in 2011 and 2014, and obtained her PhD degree in electrical engineering from Hunan University, China, in 2022. Her research interests include wind power modeling and control, decentralized/distributed voltage control and optimal operation of integrated wind power systems, and high-voltage ride-through and post-event recovery control of large-scale wind farms. Dr. Wei is an Editor of Hunan Electric Power.

Pengda Wang is a Research Associate with College of Electrical and Information Engineering, Hunan University, China. He obtained his PhD degree in Electrical Engineering from the Technical University of Denmark, Denmark, in 2022. His research interests lie in coordinated control of wind power and combined AC/DC grid, including distributed wind power modelling and control, voltage control and active/reactive power control of large-scale wind farms and combined AC/DC grid.

Canbing Li is currently a Professor with the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China. He received his B.E. and Ph.D. degrees from Tsinghua University, Beijing, China, in 2001 and 2006, respectively, both in electrical engineering. His research interests include power systems, smart grid, renewable energy with an emphasis on large-scale power system dispatch, economic and secure operation of power systems, energy efficiency and energy saving in smart grid, electric demand management of data centers, vehicle-to-grid technologies.

Vladimir Terzija is a Full Professor at the School of Engineering, Newcastle University, in the United Kingdom. He was previously a Full Professor and the Head of Laboratory of Modern Energy Systems with Skoltech, Russian Federation, EPSRC Chair Professor in power system engineering at the University of Manchester, UK, and an Assistant Professor at the University of Belgrade. Additionally, he was a Senior Specialist for switchgear and distribution automation with ABB, in Germany. His research interests include smart grid applications, wide-area monitoring, protection and control, multi-energy systems, switchgear and transient processes, ICT, data analytics, and digital signal processing applications in power systems. Prof. Terzija was the recipient of the prestigious Alexander von Humboldt Fellowship, and is the Editor-in-Chief of the International Journal of Electrical Power and Energy Systems.